Private or Public Approaches to Insuring the Uninsured: Lessons from International Experience with Private Insurance
Bibliographic record
Abstract
In the recent past a broad consensus has emerged in the United States that the best way to expand coverage of the uninsured is to use tax subsidies to encourage the purchase of private health insurance policies. Many advocates of this approach also call for replacing employment-related group policies with individual policies, and for minimizing regulation of private insurance. Those who advocate these policies, however, have rarely considered the experience that other nations have had with private health insurance. In fact most other countries have private insurance markets, and in many countries private insurance plays a significant role in financing health coverage. This study looks at the regulation of private insurance markets in five countries in which private health insurance exists as an alternative to public programs-- Chile, Australia, Germany, the Netherlands, and the United States--and three countries in which private insurance merely supplements public programs--France, Canada, and the United Kingdom. It finds that nations that have attempted to rely on private insurance to provide an alternative means of covering populations have found it necessary to establish extensive public regulatory and subsidy programs to make the private systems work. Only nations in which private insurance merely supplements public insurance do truly competitive markets exist. The article analyses these findings, concluding that true private markets for health insurance to cover entire populations are not possible, and that publicly regulated and subsidized markets do not offer efficiency advantages over public programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".